FOMO: Fairness-Oriented Multi-objective Optimization

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. Our lab takes a different approach, developing flexible optimizers that characterize the error-fairness tradeoff surface by integrating multi-objective optimization into existing machine learning models.

How FOMO works. From La Cava GECCO 2023
How FOMO works. From La Cava GECCO 2023

Code

  • FOMO: Fairness-Oriented Multi-objective Optimization
  • Interfair: Intersectional Fairness using FOMO

    • Funding

  • NIH National Library of Medicine

    • Selected Papers

    Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
    William G. La Cava (2023)
    Proceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO)
    dl.acm.org  |  arXiv  |  pdf  |  code
    Genetic programming approaches to learning fair classifiers
    William La Cava, Jason H. Moore (2020)
    Proceedings of the 2020 Genetic and Evolutionary Computation Conference
    dl.acm.org  |  arXiv  |  pdf  |  Best Paper Award